| Project/Area Number |
15K00327
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | National Institute of Advanced Industrial Science and Technology |
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Principal Investigator |
Kamishima Toshihiro 国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 主任研究員 (50356820)
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| Co-Investigator(Kenkyū-buntansha) |
赤穗 昭太郎 国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 研究グループ長 (40356340)
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2017: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2016: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2015: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | 公平性 / 推薦システム / データマイニング / 行列分解 / machine learning / data mining / fairness / recommender system / 公正性 / プライバシ / トピックモデル |
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| Outline of Final Research Achievements |
Due to the wide spread of data mining technologies, the problem of unfair decisions with respect to social sensitive information, such as gender or race, have been arising. To alleviate the problem, the techniques of fairness-aware data mining have been studied.
In this project, we develop methods of data transformation while maintaining fairness, and these methods are applied to recommendation tasks. Our main contributions are as follows: (1) We developed fairness-aware probabilistic matrix factorization model with a regularization terms, adopting Gaussian mutual information and Bhattacharyya distance. (2) We developed a topic model for collaborative filtering while maintaining independence from sensitive information.
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